An Optimized Approach for Annotation of Large Eukaryotic Genomic Sequences using Genetic Algorithm

Abstract

Detection of important functional and/or structural elements and identifying their positions in a large eukaryotic genome is an active research area. Gene is an important functional and structural unit of DNA. The computation of gene prediction is essential for detailed genome annotation. In this paper, we propose a new gene prediction technique based on Genetic Algorithm (GA) for determining the optimal positions of exons of a gene in a chromosome or genome. The correct identification of the coding and non-coding regions are difficult and computationally demanding. The proposed genetic-based method, named Gene Prediction with Genetic Algorithm (GPGA), reduces this problem by searching only one exon at a time instead of all exons along with its introns. The advantage of this representation is that it can break the entire gene-finding problem into a number of smaller subspaces and thereby reducing the computational complexity. We tested the performance of the GPGA with some benchmark datasets and compared the results with the well-known and relevant techniques. The comparison shows the better or comparable performance of the proposed method (GPGA). We also used GPGA for annotating the human chromosome 21 (HS21) using cross species comparison with the mouse orthologs.